Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Area of Science:

• Computer Science
• Cybersecurity
• Network Security

Background:

• The internet is integral to modern life, necessitating robust security measures.
• Intrusion detection systems (IDS) are vital for identifying and responding to cyber threats.
• Existing methods require continuous development for enhanced cyberspace security.

Purpose of the Study:

• To introduce a novel multivariable heuristic intrusion detection algorithm.
• To leverage flags and entropy values for improved threat detection.
• To propose default parameter values and detection thresholds for the heuristic algorithm.

Main Methods:

• Developed a multivariable heuristic intrusion detection algorithm.
• Utilized organizational data to train and validate the algorithm.
• Implemented the solution in an open-source system for testing.
• Analyzed the impact of variable updates on detection performance.

Main Results:

• The proposed heuristic algorithm demonstrated effectiveness in intrusion detection.
• Testing confirmed the reliability and performance of the new approach.
• Variable updates were shown to influence the intrusion detection process.

Conclusions:

• The novel heuristic algorithm offers a significant advancement in intrusion detection.
• The validated approach enhances the security of network environments.
• The findings support the ongoing development of effective cybersecurity solutions.